
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os


files = os.listdir(r'G:\Tutorials Notes\UdemyCourses\DataAnalysis_Projects\Covid-19\drive-download-20210623T183403Z-001')
files

['country_wise_latest.csv',
 'covid_19_clean_complete.csv',
 'day_wise.csv',
 'full_grouped.csv',
 'usa_country_wise.csv',
 'worldometer_data.csv']


def read_data(path, filename):
    return pd.read_csv(path+'/'+ filename)


path = r'G:\Tutorials Notes\UdemyCourses\DataAnalysis_Projects\Covid-19\drive-download-20210623T183403Z-001'
world_data = read_data(path,'worldometer_data.csv' )

world_data.head()


world_data.shape

(209, 16)


country_wise = read_data(path,files[0])
country_wise.head()


country_wise.shape


world_data.head()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


import plotly.express as px


# we want selected coloumns only
coloumn = ['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']

#to see the first 20 rows
for i in coloumn:
    fig = px.treemap(world_data.iloc[0:20], values = i, path =['Country/Region'], title = 'TreeMap Representation of Different countries w.r.t {}'.format(i))
    fig.show()


# for trends we have to use the lineplot over the dayise data set
day_wise = read_data(path,'day_wise.csv')
day_wise.head()


day_wise.columns

Index(['Date', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'New cases',
       'New deaths', 'New recovered', 'Deaths / 100 Cases',
       'Recovered / 100 Cases', 'Deaths / 100 Recovered', 'No. of countries'],
      dtype='object')


px.line(day_wise, x= 'Date', y = ['Confirmed', 'Deaths', 'Recovered','Active'], title ='Covid Cases w.r.t to Date', template = 'plotly_dark')


world_data.head()


#as wolrd_data don't have the feature of population to test ratio so , 
#we have to make that feature(coloumn) , with the help of population and totalTests coloumns 

world_data['pop_test_ratio'] = world_data['Population']/world_data['TotalTests']

world_data.head()


fig = px.bar(world_data.iloc[0:20], x='Country/Region' , y='pop_test_ratio', color = 'Country/Region', title='Pop to tests done ratio')
fig.show()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region', 'pop_test_ratio'],
      dtype='object')


world_data.head()


fig= px.bar(world_data.iloc[0:20],x='Country/Region', y=['Serious,Critical','TotalDeaths','TotalRecovered','ActiveCases','TotalCases'])
fig.show()


world_data.head()


fig = px.bar(world_data.iloc[0:20], x= 'TotalCases', y= 'Country/Region' , text= 'TotalCases', color= 'TotalCases')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of total confirmed cases')


#sorting in by death in descing order
sorted_worldData = world_data.sort_values(by='TotalDeaths',ascending= False)


fig = px.bar(sorted_worldData.iloc[0:20], x= 'TotalDeaths', y= 'Country/Region' , text= 'TotalDeaths', color= 'TotalDeaths')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of TotalDeaths')
fig.show()


# for this we have to seethe different cases col

labels  = world_data[0:15]['Country/Region'].values
cases = ['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']
for i in cases:
    fig =px.pie(world_data[0:15], values= i, names= labels,hole = 0.3, title = "{} Recorded w.r.t to WHO region of 15 worst affected countries".format(i))
    fig.show()


world_data.head()


world_data['death_to_confirmed'] = world_data['TotalDeaths']/world_data['TotalCases']
world_data.head()


px.bar(world_data,x='Country/Region', y = 'death_to_confirmed', title = 'Death to Confrimed Ratio')

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region
0	Afghanistan	36263	1269	25198	9796	106	10	18	3.50	69.49	5.04	35526	737	2.07	Eastern Mediterranean
1	Albania	4880	144	2745	1991	117	6	63	2.95	56.25	5.25	4171	709	17.00	Europe
2	Algeria	27973	1163	18837	7973	616	8	749	4.16	67.34	6.17	23691	4282	18.07	Africa
3	Andorra	907	52	803	52	10	0	0	5.73	88.53	6.48	884	23	2.60	Europe
4	Angola	950	41	242	667	18	1	0	4.32	25.47	16.94	749	201	26.84	Africa

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Date	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	No. of countries
0	2020-01-22	555	17	28	510	0	0	0	3.06	5.05	60.71	6
1	2020-01-23	654	18	30	606	99	1	2	2.75	4.59	60.00	8
2	2020-01-24	941	26	36	879	287	8	6	2.76	3.83	72.22	9
3	2020-01-25	1434	42	39	1353	493	16	3	2.93	2.72	107.69	11
4	2020-01-26	2118	56	52	2010	684	14	13	2.64	2.46	107.69	13

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

Q) which country has the max total death, total active cases, total recovery cases?¶

For this we have to see the world data set¶

Q> What is the trend of confirmed Deaths Recovered Active cases¶

Q> Visulise population to tests done ratio¶

Q) 20 Countries that are badly affected by corona¶

Q) Worst 20 countries having max number of confirmed cases¶

Q) Worst 20 countries having max number of total deaths¶

Q) Pie chart representation of stats of worst afferected countries?¶

Q) Deaths to confirmed Ratio¶